Data Center Network Architectures

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1 Servers Servers Servers Data Center Network Architectures Juha Salo Aalto University School of Science and Technology Abstract Data centers have become increasingly essential part of communications, thus there have been interest in understanding how to better design and manage data centers [2]. In this paper we explain a typical data center network architecture in the industry, the challenges modern data center networks encounter today and introduce proposed solutions by a recent research. KEYWORDS: data center network, cloud computing, optimization 1 Introduction According to Costa et al. [6], data centers run by Mirosoft, Yahoo, Google and Amazon host tens of thousands servers to provide services across the and the only component that has not changed during the vast development in data centers is the networking. Data Center Layer 3 Layer 2 BR BR AR AR AR AR AS AS S S S S ToR ToR ToR BR = L3 Border Router AR = L3 Access Router AS = L2 Aggr Switch S = L2 Switch ToR = Top-of-Rack Switch Figure 1: A typical data center network architecture by [9, 8] that is an adaptation of figure by Cisco [5]. Typical data center network architecture usually consists of switches and routers in two- or three-level hierarchy [1]. Figure 1 is an instance of three-level design. The following explanation for the figure is based on the papers [9, 8]. The figure contains hierarcy of top layer core routers on the top connecting to the servers in racks at the bottom. The hierarchy consists of data center layer 3, layer 2 and Top of Rack switches connecting servers. Data center layer 3 includes requests arriving from the as IP. Layer 3 access and border routers manage traffic into and out of the data center. Aggregation switches (AS) aggregate connections to layer 3 access routers (AR) and provide redundancy. Layer 2 domain contains usually several thousands servers that are isolated to server groups by virtual LAN (VLAN) partitioning. Top of Rack (ToR) switch usually connects 20 to 40 servers in the rack by 1 Gbps link. In addition, all links use Ethernet as protocol for Physical Layer. 1.1 Enterprise and Cloud Service Data Centers According to Greenberg et al. [7], major companies, such as Google, Microsoft and Yahoo have made vast scale investments for data centers supporting cloud services. Also by the same authors [7], data centers supporting cloud services differ from typical enterprise data centers. First, cloud service data centers require automation, in contrast to an enterprise data center where automation might be just partial. Second, cloud service data centers must support large economies of scale. For instance, servers might be reality. Last, cloud service data centers should scale out, not to scale-up. Scaling out distributes workload to low cost hardware, in contrast to updating high cost hardware. Instances of an enterprise data center designs are Ethane [3] and SEATTLE [12]. By Greenberg et al. [9], the enterprise networking architectures were originally developed for much smaller data centers, in contrast to the ones existing today. The limitations of the traditional architecture have resulted in several workarounds and patches for the protocols to keep up with the new demands on data centers. Greenberg et al. [8] states cloud services relation to data centers and the problem with agility. Cloud services have been a major factor for the innovation of data centers and introduces agile thinking in the data centers. Cloud services provide bulk deployments and flexible reallocation of servers to different services. However, these improvements have an economic impact. Shared data center of servers costs $12 million per month. The current data center designs prevent them being highly profitable and economical, because the designs does not provide mandatory agility. Agility in data center means responding to different demands more rapidly, instead of trying to avoid any change by preallocating resources for unkown demands. Agile design in data centers promises cost savings and improved risk management.

2 1.2 Cost structure In [7] Greenberg et al. presents data about the cost structure in a data center. Networking in data centers consumes 15% of total costs, as shown in table 1. However, networking has a more widespread impact on the whole system. Innovating the networking is the key to reduce the total costs. Amortized Component Sub-Components Cost 45% Servers CPU, memory, storage 25% Infrastructure Power distribution, cooling 15% Power draw Electrical utility costs 15% Network Links, transit, equipment Table 1: Typical data center costs [7]. By [7], the greatest portion of total costs belong to the servers. To allow efficient use of the hardware, a high level of utilization, the data centers should provide a method to dynamically grow the number of servers and allow focusing resources on optimal locations. Now the fragmentation of resources prevents the server utilization. According to Greenberg et al. [7], reducing infrastructure costs might depend on allowing scale out model of low cost servers. Scaling out in a data center might mean shifting the responsibility of expensive qualities of servers, such as failure rate from a single server to the whole system. By allowing the network architecture to scale out, the low failure rate is ensured by having multiple cheap servers, rather than a few expensive ones. By [7], power related costs are similar to the network s. IT devices consume 59% of each watt delivered, 8% to distribution losses and 33% for cooling. Cooling related costs could be reduced by allowing the data centers to run hotter, thus maybe requiring the network to be more resilient and mesh-like. Also Greenberg et al. [7] note that significant fraction of network related costs goes to networking equipment. Other portions of the total costs of the network relate to wide area networking, including traffic to end users, traffic between data centers and regional facilities. Reducing the network costs focuses on optimizing the traffic and data center placement. In this paper we focus only on the problems and solutions of the data center network architectures. Section 2 of this paper covers the main problems of network architectures. Next, section 3 evaluates different proposed solutions. Last, in conclusion we summarize the the main topics. 2 Problems with network architectures 2.1 Scalability and physical constraints The data center scaling out means addition of components that are cheap, whereas in scaling up more expensive components are upgraded and replaced to keep up with demand [9]. According to Guo et al. [10], a more attractive performanceto-price ratio can be achieved by using commodity hardware, because the per-port cost is cheaper for the commodity hardware than with the more technically advanced ones Cabling complexity could be a practical barrier for scalability, for instance in modular data center (MDC) design the long cables between the containers cause an issue as the number of containers increase [14]. Physical constraints [13], such as high density configurations in racks might lead at room level to very high power densities. Also, an efficient cooling solution is important for data center reliability and uptime. In addition, air handling systems and rack layouts affect the data center energy consumption. 2.2 Resource oversubscription and fragmentation By [9, 8] oversubscription ratio increases rapidly when moving up in the typical network architecture hierarchy, as seen in Figure 1. By the same authors, oversubscription ratio means the ratio of subscriptions to what is available. For instance, 1:20 oversubscription ratio could be 20 different 1 Gbps servers subscribed to one 1Gbps router. Ratio of 1:1 means that the subscriber can communicate with full bandwidth. In a typical network architecture the oversubscription ratio can be 1:240 for the paths crossing the top layer. Limited server-to-server capacity limits the data center performance and fragments the server pool, because unused resources can not be assigned where they are needed. To avoid this problem all applications should be placed carefully and taking the impact of the traffic in concern. However, in practice this a real challenge. Limited server-to-server capacity [9, 8] leads to designers clustering the servers near each others in the hierarchy, because the distance in the hierarchy affects the performance and cost of the communication. In addition, access routers assign IPs topologically for the layer 2, thus placing services outside layer 2-domain requires additional configuration. In todays data centers the additional configuration is avoided by reserving resources, thus wasting resources. Even reservation can not predict if a service needs more than there is reserved, resulting in allocating resources from other services. As a consequence of the dependencies resources are fragmented and isolated. 2.3 Reliability, utilization and fault tolerance Data centers suffer from poor reliability and utilization [8]. If some component of the data center fails, there must be some method to keep the data center functioning. Usually counterpart elements exists, so when an access router fails for instance the counterpart handles the load. However, this leads to elements use only 50% of maximum capacity. Multiple paths are not effectively used in current data center network architectures. Two paths at most is the limit in conventional network architectures. Techniques, such as Equal Cost Multipath Routing (ECMP) can be used to utilize multiple paths [11]. According to Al-Fares et al. [1], ECMP is currently supported by

3 switches, however several challenges are yet to be resolved, such as routing tables grows multiplicatively to number of paths used, thus presumably increasing lookup latency and cost. According to [2], links in the core of data centers compared to the average are more utilized and links on the edge are affected by higher losses on average. This research is based on the SNMP data of 19 different data centers. Due to space and operational constraints in some designs, fault tolerance and graceful performance degradation is considered extremely important [10]. Graceful performance degradation could be challenging to ensure in a typical network architectures, for instance in one incident a core switch failure lead to performance issues with ten million users for several hours [8]. 2.4 Cost According to Al-Fares et al. [1], cost is a major factor that affects the data center network architecture related decisions. Also by the same authors, one method to reduce costs is to oversubscribe data center network elements. However, oversubscription leads to problems as stated earlier. Next, we are introducing the results of a study [1] how maintaining 1:1 subscriptioin ratio relates to cost by having different type of network design. Table 2 represent the maximum cluster size supported by the most advanced 10 GigE and commodity GigE switches during a specific year. The table is divided in two different topologies, as explained in [1]: Hierarchical design contains advanced 10 GigE switches on layer 3 and as aggregation switched on layer 2. Commodity GigE switches are used on the edge in the hierarchical design. Until recently, the port density of advanced switches has limited the maximum cluster size. Also, aggregation switches did not have 10 GigE uplinks until recent new products. The price difference compared to Fat-tree design is significant. Fat-tree is a topology that supports building a largescale commodity network from commodity switches, in contrast to building a traditional hierarchical network using expensive advanced switches. In this table, fattree is just an example of such commodity networks. Fat-tree includes commodity GigE switches on all layers in the network architecture. It is worth noting the cost difference between hierarchical and fat-tree design. The total costs of Fat-tree design during the years has reduced rapidly, because of the decreasing price trend of the commodity hardware. 2.5 Incast Chen et al. [4] researched TCP Throughput Collapse, also known as Incast, which causes under-utilization of link capacity. By [4], a vast majority of data centers use TCP for communication between the nodes and Incast might occur in this type of many-to-one environment, which is different from the original assumptions TCP based its design. In other Hierarchical design Fat-tree Year 10 GigE Hosts Cost/ GigE GigE Hosts Cost/ GigE port 4,480 $25.3K 28-port 5,488 $4.5K port 7,680 $4.4K 48-port 27,648 $1.6K port 10,240 $2.1K 48-port 27,648 $1.2K port 20,480 $1.8K 48-port 27,648 $0.3K Table 2: The largest cluster sizes supported by switched with an oversubscription ratio 1:1 during [1]. words, TCP does not suit for a special data center environment with low latencies and high bandwidths, thus limits the full use of all capacity. By [4], in Incast a receiver requests data from multiple senders. Upon receiving the request the senders start transmitting data to the original receiver concurrently with the other senders. However, in the middle of the connection from sender to receiver is a bottleneck link resulting a collapse in the througput the receiver receives the data. The resulted network congestion affects all the senders using the same bottleneck link. According to Chen et al. [4], upgrading and increasing the buffer sizes of swithes and routers delays congestion, but in high latency and banwidth data center environment the buffers can still fill up in a short period of time. In addition, large buffer switches and routers are expensive. 3 Proposed network architectures 3.1 Fat-tree Al-Fares et al. [1] introduces Fat-tree, as seen in Figure 2, that enables the use of inexpensive commodity network elements for the architecture. All switching elements in the network are indentical. Also, there are always some paths to the end hosts that will use the full bandwidth. Further, the cost of Fat-tree network is less than traditional one as seen in table 2. Pod 0 Pod 1 Pod 2 Pod 3 Server Switch Core switch Link Figure 2: Fat-tree design [1]. By [1], the nature of IP/Ethernet is to establish connection between source and destination using single routing path. Single routing path leads to major performance issues in Fat-tree design. To prevent the performance issues this design proposes two-level routing tables, which can be implemented in hardware using Content-Addressable Memory

4 (CAM). According to Al-Fares et al. [1], the size of Fat-tree depends on the switch properties. Switch with 48 ports can support a network with 27,648 hosts and scaling out to support networks with over 100,000 hosts requires improved switches. In addition, wiring can be very serious challenge with Fat-tree design, however packaging and placement techniques are proposed for this issue. To fully validate Fat-tree design, further work is required [1]. However, the lack of support for performance isolation, agility and the requirement for non-existing features in commodity switches might be a major drawback concerning Fattree [8]. 3.2 Monsoon In [9] Greenberg et al. proposes a blueprint called Monsoon, a mesh-like architecture for "cloud"-services that uses commodity switches to reduce the cost and allows powerful scaling over to 100,000 servers. Monsoon improves performance by the use of Valiant Load Balancing (VLB). Figure 3 illustrates an overview of the Monsoon architecture. The architecure is divided in to Ethernet layer 2 and IP layer 3, however Monsoon focuses on the layer 2. The benefits of layer 2 include cost savings, elimination of the server fragmentation (all applications can share a huge flat address space) and avoiding disturbance of the IP-layer functionality. Layer 3 BR BR design is Ingress Server, which works with Access Routers (AR). Ingress Server is required for Monsoon load spreading and encapsulation for the VLB. 3.3 BCube, MDCube BCube [10] is a shipping-container based on modular data center (MDC) design. MDCs are formed by a frew thousands of servers that are interconnected via switches that is then packed into a 20- or 40-feet shipping-container. MDC offers short deployment time, lower cooling and manufacturing cost, and higher system and power density. Shipping container based products are already offered by major companies in the field, such as HP, Microsoft and Sun. MDCube [14] is a structure to construct mega-data centers based on containers. Containers in MDCube follow the BCube design, which connects thousands of servers inside the container. In other words, MDCube is a design to achieve a mega-data center using BCube-based containers as building blocks. 0 1 BCube k n k -1 Switch Server Link BCube k n k -1 BCube k-1 n-1 n k n k -1 Layer 2 AR AR Figure 4: BCube design [10]. LB1 LB2 A1 A2 A3 BR Border Router AR Access Router LB Load Balancer A Racks of Servers Figure 3: Monsoon design [9]. By [9], Monsoon requires layer 2 switches to have programmable control plane software, MAC-in-MAC tunneling and 16K MAC entries. Also, top-of-rack switch should handle 20 server s 1-Gbps link onto 2 10-Gbps uplinks. The upper layer switches should have 144 ports with 10-Gbps. This architecture allows over 100,000 servers with no oversubscribed links in layer 2. The load balancers (LB) can be built from commodity servers, instead of specialized and expensive hardware. IP layer 3 is responsible for dividing requests from equally to access routers (AR) by Equal Cost MultiPath (ECMP). According to Greenberg et al. [9], networking stack of a server requires replacing ARP with a user-mode process called Monsoon Agent and encapsulator, which is a new virtual Mac interface that encapsulate Ethernet frames. The Monsoon networking stack needs path information from a Directory Service. There are several ways to implement the Directory Service. Another service needed for the Monsoon Figure 4 illustrates BCube [10] server centric design, which uses only commercial-off-the-shelf (COTS) switches and commodity servers. Each server has small number of network ports, that connect to mini-switches. The routing intelligence is left for the server. The authors claim Clos topology based solutions, such as Monsoon, VL2 and Fattree do not support one-to-x (one-to-one, one-to-several and one-to-all) well, in contrast to BCube. In addition, results show that BCube offers more graceful performance degradation than typical network architectures. Wu et al. [14] proposes MDCube that provides good faulttolerance, high network capacity for mega-data centers and manageable cabling complexity. BCube containers in MD- Cube are interconnected by using high-speed interfaces of switches in BCube. BCube containers acts as a virtual node in MDCube with the MDCube switches being virtual interfaces to these virtual nodes. MDCube is a server-centric design, thus leaving the logic to the servers. MDCube seems to require networking stack modifications for load balancing and fault tolerant routing as in server-centric manner. Routing to external networks is provided by reserving switches and servers in BCubes for the external connections. According to Wu et al. [14], MDCube inter-container cables number is reduced almost magnitude of two orders when compared with mega-data centers constructed from single structure designs such as BCube or Fat-tree.

5 0.*0 0.*1 0.1* 0.0* *0 1.*1 1.1* 1.0* *0 2.*1 3.1* 3.0* 4.1* 4.0* 4.*0 3.* *1 3.* Server Switch Link 2.1* 2.0* locations, thus providing agility. LAs are assigned for all switches and interfaces, while AAs are only used in applications. According to Greenberg et al [8], one VL2 design principle is to allow implementation on existing hardware, so that VL2 could be taken in use even today. The authors evaluated VL2 performance by a working prototype. The results indicate that VL2 is efficient and achieves high load balancing fairness. In addition, rough cost estimates also indicate that a typical network without oversubscribed links costs 14 times more than equivalent VL2 network. However, the authors of MDCube [14] state that VL2 design is still expensive, since they use rather high end switches in the layer 2. For instance building a 1 million server network would require port 10Gbps intermediate and aggregate switches. 3.4 VL2 Figure 5: Example MDCube design [14]. Greenberg et al. [8] introduces VL2, a network architecture that uses Valiant Load Balancing (VLB) for traffic spreading, address resolution supporting large server pools and flat addressing to avoid fragmentation of resources. The actual topology provides path diversity. Overall VL2 is promised to solve many current problems by offering agility, since it creates an illusion of a single whole data center wide layer-2 switch by creating a virtual layer. Also, VL2 eliminates the need for overscubscriping links in the network by the network design. Link-state, LA Int Int AG AG ToR Funglible pool, 20 AA servers Int Intermediate Switch AG Aggregation Switch ToR Top-of-Rack Switch Figure 6: VL2 design [8]. Apparently similarly to Monsoon, by [8] VL2 requires a directory service and server agent for VL2 addressing and routing. Also, it seems VL2 requires changes to servers network stacks to enable VL2 adddressing and routing design. Key concepts in VL2 addressing and routing are application-specific addresses (AAs) and location-specific addresses (LAs) that are used to separate server name from 3.5 Fixing Incast Chen et al. [4] research for TCP Incast solutions focused on TCP-based methods, preffering existing technology over creating a new one. It might also be more cost efficient and more attractive for the data center operators. An instance of non TCP-based method is a global scheduler on the application level. Global scheduler would require modifications to all the applications, but as said earlier this type of solutions are unattractive because of the complexity and effort. In [4], dynamics of Incast was studied and experiments were conducted. The data indicate differencies between the related work of this area and the observations from the conducted experiments. In addition, to explain the trends observed an analytical model was introduced. 4 Conclusion In this paper we first introduced the current trend in the data center industry among some information about the cost structure. Next, we explained the problems with todays data center network architectures, including scalability, physical constraints, resource oversubscription and fragmentation, reliability, utilization, fault tolerance, cost and Incast. Last, we introduced some recently proposed solutions for the problems. We covered Monsoon, VL2, Fat-tree and MDCube. Each of the proposed solutions had their strengths and weaknesses, however our quick guess is that the ones more favorable for the industry are the ones that are deployable even today and require minimal effort for the existing hardware. One common trend with the proposed solutions seemed to be the use of cheap commodity devices over expensive ones. References [1] M. Al-Fares, A. Loukissas, and A. Vahdat. A scalable, commodity data center network architecture. SIG- COMM Comput. Commun. Rev., 38(4):63 74, [2] T. Benson, A. Anand, A. Akella, and M. Zhang. Understanding data center traffic characteristics. In WREN

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